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Robot Ecology: Constraint-Based Design for Long-Duration Autonomy [Kõva köide]

  • Formaat: Hardback, 360 pages, kõrgus x laius: 235x156 mm, 101 b/w illus. 2 tables.
  • Ilmumisaeg: 28-Dec-2021
  • Kirjastus: Princeton University Press
  • ISBN-10: 069121168X
  • ISBN-13: 9780691211688
Teised raamatud teemal:
  • Formaat: Hardback, 360 pages, kõrgus x laius: 235x156 mm, 101 b/w illus. 2 tables.
  • Ilmumisaeg: 28-Dec-2021
  • Kirjastus: Princeton University Press
  • ISBN-10: 069121168X
  • ISBN-13: 9780691211688
Teised raamatud teemal:

A revolutionary new framework that draws on insights from ecology for the design and analysis of long-duration robots

Robots are increasingly leaving the confines of laboratories, warehouses, and manufacturing facilities, venturing into agriculture and other settings where they must operate in uncertain conditions over long timescales. This multidisciplinary book draws on the principles of ecology to show how robots can take full advantage of the environments they inhabit, including as sources of energy.

Magnus Egerstedt introduces a revolutionary new design paradigm—robot ecology—that makes it possible to achieve long-duration autonomy while avoiding catastrophic failures. Central to ecology is the idea that the richness of an organism’s behavior is a function of the environmental constraints imposed by its habitat. Moving beyond traditional strategies that focus on optimal policies for making robots achieve targeted tasks, Egerstedt explores how to use survivability constraints to produce both effective and provably safe robot behaviors. He blends discussions of ecological principles with the development of control barrier functions as a formal approach to constraint-based control design, and provides an in-depth look at the design of the SlothBot, a slow and energy-efficient robot used for environmental monitoring and conservation.

Visionary in scope, Robot Ecology presents a comprehensive and unified methodology for designing robots that can function over long durations in diverse natural environments.

Preface xiii
I Long-Duration Autonomy
1 Introduction
3(17)
1.1 Long-Duration Autonomy
3(7)
1.1.1 Lessons from Mars
4(2)
1.1.2 Operations Beyond a Single Battery Charge
6(2)
1.1.3 On the Value of Slowness
8(2)
1.2 Survivability
10(5)
1.2.1 Costs and Constraints
11(2)
1.2.2 Robots that Do (Almost) Nothing
13(2)
1.3 Coupling Between Environment and Robot
15(3)
1.3.1 Ecosystems
16(1)
1.3.2 Natural and Engineered Environments
17(1)
1.4 Summarizing and Looking Ahead
18(2)
2 Survival of the Robots
20(34)
2.1 Behavior-Based Robotics
21(10)
2.1.1 Behaviors in Robots and Animals
22(4)
2.1.2 Arbitration Mechanisms
26(5)
2.2 Multi-Robot Behaviors
31(12)
2.2.1 Flocking and Swarming
31(2)
2.2.2 Coordinated Control
33(4)
2.2.3 Formation Control
37(3)
2.2.4 Coverage Control
40(3)
2.3 The Combinatorics of the Real World
43(11)
2.3.1 Elephants Don't Play Chess
43(3)
2.3.2 Technology Readiness Levels
46(2)
2.3.3 Constraints and Laws of Robotics
48(6)
3 Ecological Connections
54(37)
3.1 Organisms and Environments
56(10)
3.1.1 Consumers and Resources
56(5)
3.1.2 Niches and Fitness Sets
61(5)
3.2 Interactions
66(12)
3.2.1 Fecundity and Survival
66(2)
3.2.2 Competition
68(4)
3.2.3 Predators and Parasites
72(4)
3.2.4 Social Behaviors
76(2)
3.3 Ecologically Inspired Constraints
78(13)
3.3.1 Ideal Free Distributions
79(2)
3.3.2 Competitive and Cooperative Interactions
81(4)
3.3.3 Thermoregulation and Task Persistification
85(1)
3.3.4 Towards Robot Ecology
86(5)
II Constraint-Based Control
4 Constraints and Barriers
91(33)
4.1 Forward Invariance
92(8)
4.1.1 Collision-Avoidance
92(3)
4.1.2 Remaining Safe Forever
95(1)
4.1.3 Nagumo and the Comparison Lemma
96(4)
4.2 Control Barrier Functions
100(8)
4.2.1 Optimization-Based Control
101(3)
4.2.2 Further Considerations
104(1)
4.2.3 Survivability Constraints
105(3)
4.3 Collision-Avoidance
108(6)
4.3.1 Centralized Safety Barriers
109(3)
4.3.2 Decentralized Safety Barriers
112(2)
4.4 Safe Learning
114(10)
4.4.1 Learning Barrier Functions
118(3)
4.4.2 Applications to Aerial Robotics
121(3)
5 Persistification of Robotic Tasks
124(24)
5.1 Energy Dynamics
125(8)
5.1.1 Environmental Interactions
125(5)
5.1.2 Task Persistification
130(3)
5.2 Variations on the CBF Theme
133(6)
5.2.1 High Relative Degree Barrier Functions
133(4)
5.2.2 Time Varying Barrier Functions
137(1)
5.2.3 Solving the Persistification Problem
138(1)
5.3 Environmental Monitoring
139(9)
5.3.1 Exploration
140(3)
5.3.2 Coverage
143(5)
6 Composition of Barrier Functions
148(31)
6.1 Boolean Composition
149(4)
6.1.1 Disjunctions and Conjunctions
149(2)
6.1.2 Secondary Operations
151(2)
6.2 Non-Smooth Barrier Functions
153(6)
6.2.1 Generalized Gradients
155(1)
6.2.2 Set-Valued Lie Derivatives
156(3)
6.3 Min/Max Barrier Functions
159(8)
6.3.1 Boolean Composition of Barrier Functions
161(4)
6.3.2 Navigation Example
165(2)
6.4 Connectivity-Preserving Coordinated Control
167(12)
6.4.1 Composite Safety and Connectivity Barrier Functions
168(3)
6.4.2 Maintaining Dynamic Connectivity Graphs
171(8)
III Robots in the Wild
7 Robot Ecology
179(42)
7.1 Constraints From Behavioral Ecology
181(12)
7.1.1 Constituent Constraints
181(6)
7.1.2 Survivability Constraints
187(6)
7.2 Goal-Driven Behaviors
193(11)
7.2.1 From Gradient Descent to Barrier-Based Descent
195(4)
7.2.2 Costs as Constraints
199(3)
7.2.3 Finite-Time Performance
202(2)
7.3 Goal-Driven Multi-Robot Systems
204(8)
7.3.1 Formation and Coverage Control Revisited
207(3)
7.3.2 Sequential Composition of Behaviors
210(2)
7.4 Putting It All Together
212(9)
7.4.1 A Purposeful Yet Safe Expenditure of Energy
212(6)
7.4.2 The End Game
218(3)
8 Environmental Monitoring
221(27)
8.1 Monitoring in Natural Environments
222(9)
8.1.1 Biodiversity
223(4)
8.1.2 Microclimates and Ecological Niche Models
227(2)
8.1.3 Under the Tree Canopies
229(2)
8.2 Wire-Traversing Robots
231(8)
8.2.1 Design Considerations
232(3)
8.2.2 Mechanical Design
235(4)
8.3 The SlothBot
239(9)
8.3.1 Motion Planning and Control
240(3)
8.3.2 Long-Duration Deployment
243(5)
9 Autonomy-on-Demand
248(39)
9.1 Recruitable Robots
249(8)
9.1.1 Task Specifications
249(2)
9.1.2 Remote Access Control in the Robotarium
251(6)
9.2 The Robotarium: An Autonomy-on-Demand Multi-Robot Platform
257(11)
9.2.1 The Impetus Behind Remote-Access Robotics
257(2)
9.2.2 Testbed Design
259(4)
9.2.3 Safety and Robust Barrier Functions
263(5)
9.3 Remote Experimentation
268(19)
9.3.1 Submission Process
270(2)
9.3.2 The Robotarium Userbase
272(5)
9.3.3 User Experiments
277(2)
9.3.4 Case Studies
279(8)
Bibliography 287(40)
Index 327
Magnus Egerstedt is the Stacey Nicholas Dean of Engineering in the Samueli School of Engineering at the University of California, Irvine. He is the coauthor of Graph Theoretic Methods in Multiagent Networks and Control Theoretic Splines: Optimal Control, Statistics, and Path Planning (both Princeton).